Apparatus and method for performing artificial neural network forward operations

Abstract

The invention discloses an operation device and method, and the device comprises a floating point data statistical module which is used for the statistical analysis of various types of needed data, and obtaining the exponential bit offset and the length EL of an exponential bit; a floating point data conversion module which is used for realizing conversion from a long-digit floating point data type to a short-digit floating point data type according to the exponential bit offset and the length EL of the exponential bit; and a floating point data operation module which is used for performing artificial neural network forward operation on the short-digit floating point data. According to the operation device, data in forward operation of the multilayer artificial neural network is represented by the short-digit floating points, and the corresponding floating point operation modules are used, so that forward operation of the short-digit floating points of the artificial neural network isrealized, and the performance-to-power consumption ratio of hardware is greatly improved.

Description

technical field [0001] The present invention relates to the technical field of artificial neural network, and more particularly relates to a device and method for performing forward operation of artificial neural network. Background technique [0002] Multi-layer artificial neural networks are widely used in the fields of pattern recognition, image processing, function approximation, and optimization calculations. In recent years, multi-layer artificial neural networks have been favored by academic circles and industry is getting more and more attention. [0003] Traditional artificial neural network forward operations usually use 32-bit floating-point data types for data storage, but in most forward operations of artificial neural networks, the same type of data will be concentrated within a certain data range. However, the range of data that can be represented by 32-bit floating point is much larger than the data range of the same type of data in the forward operation. Th...

AI Technical Summary

Problems solved by technology

[0003] Traditional artificial neural network forward operations usually use 32-bit floating-point data types for data storage, but in most forward operations of artificial neural networks, the same type of data will be concentrated within a certain data range. However, the data range that can be represented by 32-bit floating point is much larger than the data range of the same type of data in the forward operation. There is a lot of redundancy in the use of 32-bit floating point data type, which increases the area overhead of hardware.

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